bulk commit

.Rbuildignore

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+^rbmi\.helpers\.Rproj$
+^\.Rproj\.user$
+^LICENSE\.md$
+^README\.Rmd$
+^data-raw$
+^doc$
+^Meta$
+^.gitlab-ci.yml$
+^docs$
+^.*\.Rproj$

.gitignore

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+.Rproj.user
+.Rhistory
+.Rdata
+.httr-oauth
+.DS_Store
+.quarto
+inst/doc
+/doc/
+/Meta/
+scratch.R
+.gitlab-ci.yml

DESCRIPTION

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+Package: rbmiUtils
+Title: Utility functions to work with and extend the rbmi package 
+Version: 0.0.1
+Authors@R: c( 
+    person("Mark", "Baillie", email = "mark.baillie@novartis.com", role = c("aut", "cre"),
+           comment = c(ORCID = "0000-0002-5618-0667")),
+    person("Tobias", "Mütze", email = "tobias.muetze@novartis.com", role = c("aut"),
+           comment = c(ORCID = "0000-0002-4111-1941")),
+    person("Jack", "Talboys", email = "jack.talboys@novartis.com", role = c("ctb")),
+    person("Lukas A.", "Widmer", role = "ctb", comment = c(ORCID = "0000-0003-1471-3493"))
+    )
+Description: This package provides additional functionality for rbmi to enable trial analysis and reporting aligned with CDISC ADaM.
+License: GPL (>= 3)
+Encoding: UTF-8
+Roxygen: list(markdown = TRUE)
+RoxygenNote: 7.3.2
+Suggests: 
+    knitr,
+    rmarkdown,
+    spelling,
+    testthat (>= 3.0.0),
+    tidyr,
+    readr, 
+    ggplot2,
+    beeca
+Config/testthat/edition: 3
+Language: en-US
+Imports: 
+    assertthat,
+    dplyr,
+    purrr,
+    rbmi,
+    rlang
+VignetteBuilder: knitr
+Depends: 
+    R (>= 2.10)
+LazyData: true

NAMESPACE

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+# Generated by roxygen2: do not edit by hand
+
+export(analyse_mi_data)
+export(get_imputed_data)
+export(tidy_pool_obj)

NEWS.md

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+# rbmiUtils (development version)
+
+* Initial CRAN submission.
+
+# rbmiUtils 0.0.1
+
+* tbc

R/ADEFF.R

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+#' Example efficacy trial dataset
+#'
+#' A simplified example of a simulated trial dataset, with missing data.
+#'
+#' @format `ADEFF`
+#' A data frame with 1,000 rows and 10 columns:
+#' \describe{
+#'    \item{USUBJID}{Unique subject identifier}
+#'    \item{AVAL}{Primary outcome variable}
+#'    \item{TRT01P}{Planned treatment}
+#'    \item{STRATA}{Stratification at randomisation}
+#'    \item{REGION}{Stratification by region}
+#'    \item{REGIONC}{Stratification by region, numeric code}
+#'    \item{BASE}{Baseline value of primary outcome variable}
+#'    \item{CHG}{Change from baseline}
+#'    \item{AVISIT}{Visit number}
+#'    \item{PARAM}{Analysis parameter name}
+#' }
+#'
+"ADEFF"

R/ADMI.R

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+#' Example multiple imputation trial dataset
+#'
+#' A simplified example of a simulated trial ADMI dataset
+#'
+#' @format `ADMI`
+#' A data frame with 100,000 rows and 12 columns:
+#' \describe{
+#'    \item{USUBJID}{Unique patient identifier}
+#'    \item{STRATA}{Stratification at randomisation}
+#'    \item{REGION}{Stratification by region}
+#'    \item{REGIONC}{Stratification by region, numeric code}
+#'    \item{TRT}{Planned treatment}
+#'    \item{BASE}{Baseline value of primary outcome variable}
+#'    \item{CHG}{Change from baseline}
+#'    \item{AVISIT}{Visit number}
+#'    \item{IMPID}{Imputation number identifier}
+#'    \item{CRIT1FLN}{Responder criteria (binary)}
+#'    \item{CRIT1FL}{Responder criteria (categorical)}
+#'    \item{CRIT}{Responder criteria (definition)}
+#' }
+#'
+"ADMI"

R/analyse_mi_data.R

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+#' Apply Analysis Function to Multiple Imputed Datasets
+#'
+#' This function applies an analysis function (e.g., ANCOVA) to imputed datasets and stores the results for later pooling. It is designed to work with multiple imputed datasets and apply a given analysis function to each imputation iteration.
+#'
+#' @param data A data frame containing the imputed datasets. The data frame should include a variable (e.g., `IMPID`) that identifies distinct imputation iterations.
+#' @param vars A list specifying key variables used in the analysis (e.g., `subjid`, `visit`, `group`, `outcome`). Required.
+#' @param method A character string or object specifying the method used for analysis (e.g., Bayesian imputation). Defaults to `NULL`.
+#' @param fun A function that will be applied to each imputed dataset. Defaults to `rbmi::ancova`. Must be a valid analysis function.
+#' @param delta A `data.frame` used for delta adjustments, or `NULL` if no delta adjustments are needed. Defaults to `NULL`.
+#' @param ... Additional arguments passed to the analysis function `fun`.
+#'
+#' @details
+#' The function loops through distinct imputation datasets (identified by `IMPID`), applies the provided analysis function `fun`, and stores the results for later pooling. If a `delta` dataset is provided, it will be merged with the imputed data to apply the specified delta adjustment before analysis.
+#'
+#' @return An object of class `analysis` containing the results from applying the analysis function to each imputed dataset.
+#'
+#' @examples
+#' # Example usage with an ANCOVA function
+#' library(dplyr)
+#' library(rbmi)
+#'
+#' data("ADMI")
+#' N_IMPUTATIONS <- 100
+#' BURN_IN <- 200
+#' BURN_BETWEEN <- 5
+#'
+#' # Convert key columns to factors
+#' ADMI$TRT <- factor(ADMI$TRT, levels = c("Placebo", "Drug A"))
+#' ADMI$USUBJID <- factor(ADMI$USUBJID)
+#' ADMI$AVISIT <- factor(ADMI$AVISIT)
+#'
+#' # Define key variables for ANCOVA analysis
+#'  vars <- set_vars(
+#'   subjid = "USUBJID",
+#'   visit = "AVISIT",
+#'   group = "TRT",
+#'   outcome = "CHG",
+#'   covariates = c("BASE", "STRATA", "REGION")  # Covariates for adjustment
+#'  )
+#'
+#' # Specify the imputation method (Bayesian) - need for pool step
+#' method <- rbmi::method_bayes(
+#'   n_samples = N_IMPUTATIONS,
+#'   burn_in = BURN_IN,
+#'   burn_between = BURN_BETWEEN
+#'   )
+#'
+#' # Perform ANCOVA Analysis on Each Imputed Dataset
+#' ana_obj_ancova <- analyse_mi_data(
+#'   data = ADMI,
+#'   vars = vars,
+#'   method = method,
+#'   fun = ancova,  # Apply ANCOVA
+#'   delta = NULL   # No sensitivity analysis adjustment
+#' )
+#'
+#' @export
+analyse_mi_data <- function(data = NULL, vars = NULL, method = NULL, fun = rbmi::ancova, delta = NULL, ...){
+
+  # Check for missing inputs
+  if (is.null(data))
+    stop("`data` cannot be NULL.")
+
+  # check IMPID is in data
+  if (!"IMPID" %in% names(data))
+    stop("`data` must contain a variable `IMPID` to identify distinct imputation iterations.")
+
+  if (is.null(vars))
+    stop("`vars` cannot be NULL. Specify key variables.")
+
+  ## asset function
+  assertthat::assert_that(
+    is.function(fun),
+    msg = "`fun` must be a function"
+  )
+
+  ## check on delta
+  assertthat::assert_that(
+    is.null(delta) | is.data.frame(delta),
+    msg = "`delta` must be NULL or a data.frame"
+  )
+
+  ## check delta has correct variables and then apply
+  if (!is.null(delta)) {
+    expected_vars <- c(
+      vars$subjid,
+      vars$visit,
+      "delta"
+    )
+    assertthat::assert_that(
+      all(expected_vars %in% names(delta)),
+      msg = sprintf(
+        "The following variables must exist witin `delta`: `%s`",
+        paste0(expected_vars, collapse = "`, `")
+      )
+    )
+
+    ## apply delta to data set adding to outcome
+    data <- data |>
+      dplyr::left_join(delta, by = c(vars$subjid, vars$visit, vars$group)) |>
+      dplyr::mutate(!!rlang::sym(vars$outcome) := .data[[vars$outcome]] + .data$delta)
+  }
+
+  # Loop through distinct imputation data sets based on IMPID in a single data frame
+  results <- data |>
+    dplyr::group_split(IMPID) |>
+    lapply(function(dat_subset, ...) {
+      # Perform analysis on the subset of data corresponding to each imputation
+      fun(dat_subset, vars, ...)
+    }, ...)
+
+
+  fun_name <- deparse(substitute(fun))
+
+  if (length(fun_name) > 1) {
+    fun_name <- "<Anonymous Function>"
+  } else if (is.null(fun_name)) {
+    fun_name <- "<NULL>"
+  }
+
+  ret <- as_analysis2(
+    results = results,
+    fun_name = fun_name,
+    delta = delta,
+    fun = fun,
+    method = method
+  )
+
+  return(ret)
+}
+
+
+#' Construct an rbmi `analysis` object
+#'
+#' @description
+#' This is a helper function to create an analysis object that stores the
+#' results from multiple imputation analyses. It validates the results and
+#' ensures proper class assignment.
+#'
+#' This is a modification of the rbmi::as_analysis function.
+#'
+#' @param results A list containing the analysis results for each imputation.
+#' @param method The method object used for the imputation.
+#' @param delta Optional. A delta dataset used for adjustment.
+#' @param fun The analysis function that was used.
+#' @param fun_name The name of the analysis function (used for printing).
+#'
+#' @return An object of class `analysis` with the results and associated metadata.
+as_analysis2 <- function(results, method, delta = NULL, fun = NULL, fun_name = NULL) {
+
+  next_class <- switch(class(method)[[2]],
+                       bayes = "rubin",
+                       approxbayes = "rubin",
+                       condmean = ifelse(
+                         method$type == "jackknife",
+                         "jackknife",
+                         "bootstrap"
+                       ),
+                       bmlmi = "bmlmi"
+  )
+
+  assertthat::assert_that(
+    is.list(results),
+    length(next_class) == 1,
+    is.character(next_class),
+    next_class %in% c("jackknife", "bootstrap", "rubin", "bmlmi")
+  )
+
+  x <- list(
+    results = rbmi::as_class(results, c(next_class, "list")),
+    delta = delta,
+    fun = fun,
+    fun_name = fun_name,
+    method = method
+  )
+  class(x) <- c("analysis", "list")
+
+  return(x)
+}

R/rbmiUtils-package.R

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+utils::globalVariables(
+  c(
+    ".data",
+    ":=",
+    "IMPID",
+    "description",
+    "est",
+    "lci",
+    "lsm_type",
+    "original_id",
+    "parameter",
+    "parameter_type",
+    "pval",
+    "se",
+    "uci",
+    "visit"
+  )
+)
+
+#' @keywords internal
+"_PACKAGE"
+
+## usethis namespace: start
+## usethis namespace: end
+NULL